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Like many highly variable human traits, more than a dozen genes are known to contribute to the full range of skin color. However, the historical bias in favor of genetic studies in European and European‐derived populations has blinded us to the magnitude of pigmentation's complexity. As deliberate efforts are being made to better characterize diverse global populations and new sequencing technologies, better measurement tools, functional assessments, predictive modeling, and ancient DNA analyses become more widely accessible, we are beginning to appreciate how limited our understanding of the genetic bases of human skin color have been. Novel variants in genes not previously linked to pigmentation have been identified and evidence is mounting that there are hundreds more variants yet to be found. Even for genes that have been exhaustively characterized in European populations like MC1R, OCA2, and SLC24A5, research in previously understudied groups is leading to a new appreciation of the degree to which genetic diversity, epistatic interactions, pleiotropy, admixture, global and local adaptation, and cultural practices operate in population‐specific ways to shape the genetic architecture of skin color. Furthermore, we are coming to terms with how factors like tanning response and barrier function may also have influenced selection on skin throughout human history. By examining how our knowledge of pigmentation genetics has shifted in the last decade, we can better appreciate how far we have come in understanding human diversity and the still long road ahead for understanding many complex human traits. [ABSTRACT FROM AUTHOR]

Traditional clustering and visualization approaches in human genetics often operate under frameworks that assume inherent, discrete groupings 1,2 . These methods can inadvertently simplify multifaceted relationships, functioning to entrench the idea of typological groups 3 . We introduce a network-based pipeline and visualization tool grounded in relational thinking 4 , which constructs networks from a variety of genetic similarity metrics. We identify communities at multiple resolutions, departing from typological models of analysis and interpretation that categorize individuals into a (predefined) number of sets. We applied our pipeline to a dataset merged from the 1000 Genomes and Human Genome Diversity Project 5 , revealing the limitations of traditional groupings and capturing the complexities introduced by demographic events and evolutionary processes. This method embraces the context-specificity of genetic similarities that are salient depending on the question, markers of interest, and study individuals. Different numbers of communities are revealed depending on the resolution chosen and metric used, underscoring a fluid spectrum of genetic relationships and challenging the notion of universal categorization. We provide a web application ( https://sohail-lab.shinyapps.io/GG-NC/ ) for interactive visualization and engagement with these intricate genetic landscapes.